With CDISC Adoption by FDA, Managing Clinical Trial Metadata with Semantic Technology Has Emerged Front and Center!

Posted by Dan Kesack on Dec 4, 2014 4:44:00 PM

As FDA outlines, “the submission of standardized study data enhances a reviewer’s ability to more fully understand and characterize the efficacy and safety of a medical product,” and adopted CDISC standards based on semantic technology to be the standards for submitting and using study data. They further “envision a semantically interoperable and sustainable submission environment that serves both regulated clinical research and health care.”

health-521386_640.jpgPharma and healthcare companies are working with companies like ours to address submission needs and improve the analysis and data management for clinical trials. Below we outline the problem and some of the work we are doing.

Data groups within the pharmaceutical industry are no strangers to the need to manage clinical study metadata. At the same time, they need to expand the scope of internal metadata to meet the practical needs of those collecting data, creating datasets, and performing analysis. When mismanaged, clinical trial metadata management becomes a burdensome, time consuming activity performed only to meet regulatory requirements. Done correctly, regulatory reporting is a by-product, and clinical trial metadata is an asset to the entire organization.

Big pharma is increasingly turning to semantic technology to handle their metadata management needs. The Clinical Data Interchange Standards Consortium (CDISC) releases clinical metadata standards in RDF format, which is central to semantic web technology. Using semantics, metadata is linked across standards, both internal and external. Different standards are free to evolve without coordination, while the relationships between the metadata is maintained.

Metadata managers can decide how changes in standards impact their organization. Internal metadata is semantically linked to the external standards. When the external standards are updated, managers can perform impact analysis to identify which internal metadata is effected, and update the affected metadata as necessary.

Query across disparate datasets. Using both internal and external metadata standards, researchers can identify studies with common characteristics and combine datasets to perform comprehensive analysis across studies. Linked metadata to existing datasets and data sources to promote search and discovery.

Versioning and lineage. Track changes in metadata over time and archive previous versions for future reference. Customize part of a standard for more specific use within the organization while maintaining the linkage to the external standard.

Bring your own standard, report with existing standards. By maintaining a semantic link between internal metadata and the external standards, data can be tagged with meaning specific to a group, but can still be exported in a format that is required by regulators.

Workflows and governance. Semantic technology is a natural platform for workflows and metadata governance. Metadata elements can be semantically linked to workflow tasks, statuses, data stewards or owners, and permissions for building powerful custom data governance applications that fit the needs of your organization.

Using spreadsheets or traditional databases to manage clinical metadata is static and inflexible. Traditional metadata management tools don’t handle the combination of internal and external standards common to clinical metadata. Semantic technology is a powerful platform for clinical metadata management, providing a single application for maintenance, search, reporting, and governance.

To learn more, download our whitepaper "Keeping the Big Data Promise: Data Lakes for Life Sciences".

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Tags: CDISC, Metadata, Semantics, Data Management, Clincal Trials

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